Abstract
This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.
Original language | English (US) |
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Pages (from-to) | 43-49 |
Number of pages | 7 |
Journal | Neurocomputing |
Volume | 169 |
DOIs | |
State | Published - May 29 2015 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01ASJC Scopus subject areas
- Artificial Intelligence
- Cognitive Neuroscience
- Computer Science Applications